README.md

metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:647236
  - loss:MultipleNegativesSymmetricRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
  - source_sentence: essence multi task concealer 15 natural nude
    sentences:
      - pure oxygen 20 vol
      - essence
      - face make-up
  - source_sentence: faber castell jumbo colored pencil, metallic copper
    sentences:
      - ' faber castell colored pencil'
      - pencil
      - a4 photographic paper, 5 colors, 100 sheets, 80 gsm
  - source_sentence: gedo & the champ
    sentences:
      - children book
      - ' book'
      - diary of a wimpy kid do-it-youself book
  - source_sentence: green track suit
    sentences:
      - outfit
      - green track suit
      - tres
  - source_sentence: must kindergarten backpack mermazing 2 cases
    sentences:
      - crescent stand with 3 dates plate gold
      - school supplies
      - bag
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy
model-index:
  - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
    results:
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: cosine_accuracy
            value: 0.9700284004211426
            name: Cosine Accuracy

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: sentence-transformers/all-MiniLM-L6-v2
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("LamaDiab/v2MiniLM-V22Data-128ConstantBATCH-SemanticEngine")
# Run inference
sentences = [
    'must kindergarten backpack mermazing 2 cases',
    'school supplies',
    'crescent stand with 3 dates plate gold',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.5733, -0.2166],
#         [ 0.5733,  1.0000, -0.0339],
#         [-0.2166, -0.0339,  1.0000]])

Evaluation

Metrics

Triplet

• Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.97

Training Details

Training Dataset

Unnamed Dataset

• Size: 647,236 training samples
• Columns: anchor, positive, and itemCategory
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	itemCategory
  type	string	string	string
  details	min: 3 tokens mean: 11.56 tokens max: 50 tokens	min: 3 tokens mean: 4.55 tokens max: 12 tokens	min: 3 tokens mean: 3.91 tokens max: 9 tokens

• Samples:

  anchor	positive	itemCategory
  petrol samsung galaxy	smart phone	smart phone
  must trolley bag must true football 4 cases	wheels cover backpack	bag
  sanpellegrino chino is a bold and refreshing italian beverage with a unique bittersweet flavor made from herbal extracts and citrus best served chilled for a distinctive taste experience	chino can drink	beverage

• Loss: MultipleNegativesSymmetricRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 9,509 evaluation samples
• Columns: anchor, positive, negative, and itemCategory
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative	itemCategory
  type	string	string	string	string
  details	min: 3 tokens mean: 9.63 tokens max: 43 tokens	min: 3 tokens mean: 6.61 tokens max: 150 tokens	min: 3 tokens mean: 9.58 tokens max: 46 tokens	min: 3 tokens mean: 3.88 tokens max: 10 tokens

• Samples:

  anchor	positive	negative	itemCategory
  pilot mechanical pencil progrex h-127 - 0.7 mm	pencil	artist pen brush tip 1.5m gold no.250	pencil
  superior drawing marker -pen - set of 12 colors - 2 nib	superior	notte 11-101 a5 stapled squared notebook, 60 sheets, cardboard cover, 60 grams, 148 x 210 mm, turkish	marker
  first person singular author: haruki murakami	haruki murakami book	yellow dinosaur assembling game	literature and fiction

• Loss: MultipleNegativesSymmetricRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• learning_rate: 2e-05
• weight_decay: 0.001
• num_train_epochs: 6
• warmup_ratio: 0.2
• fp16: True
• dataloader_num_workers: 1
• dataloader_prefetch_factor: 2
• dataloader_persistent_workers: True
• push_to_hub: True
• hub_model_id: v2MiniLM-V22Data-128ConstantBATCH-SemanticEngine
• hub_strategy: all_checkpoints

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 2e-05
• weight_decay: 0.001
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 6
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.2
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: False
• fp16: True
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 1
• dataloader_prefetch_factor: 2
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: True
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: True
• resume_from_checkpoint: None
• hub_model_id: v2MiniLM-V22Data-128ConstantBATCH-SemanticEngine
• hub_strategy: all_checkpoints
• hub_private_repo: None
• hub_always_push: False
• hub_revision: None
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• liger_kernel_config: None
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	cosine_accuracy
0.0002	1	3.9486	-	-
0.1977	1000	3.172	0.5803	0.9392
0.3955	2000	2.6021	0.5286	0.9490
0.5932	3000	2.1529	0.4992	0.9545
0.7910	4000	1.3847	0.4794	0.9547
0.9887	5000	0.9942	0.4432	0.9548
1.1864	6000	1.4574	0.4378	0.9597
1.3841	7000	1.3286	0.4299	0.9629
1.5817	8000	1.2024	0.4179	0.9646
1.7794	9000	1.1554	0.4171	0.9648
1.9771	10000	1.0769	0.4174	0.9635
2.1747	11000	0.9984	0.4163	0.9677
2.3724	12000	0.9714	0.4026	0.9676
2.5701	13000	0.9208	0.4087	0.9674
2.7677	14000	0.9027	0.3975	0.9681
2.9654	15000	0.8854	0.4018	0.9680
3.1631	16000	0.8299	0.4085	0.9688
3.3607	17000	0.8103	0.3995	0.9687
3.5584	18000	0.7853	0.3974	0.9677
3.7561	19000	0.7734	0.3981	0.9685
3.9537	20000	0.7758	0.3996	0.9685
4.1514	21000	0.7463	0.4009	0.9690
4.3491	22000	0.7212	0.4014	0.9688
4.5467	23000	0.7312	0.3967	0.9695
4.7444	24000	0.7175	0.3956	0.9695
4.9421	25000	0.7196	0.3931	0.9701
5.1398	26000	0.6815	0.3936	0.9690
5.3374	27000	0.6875	0.3936	0.9695
5.5351	28000	0.6955	0.3948	0.9692
5.7328	29000	0.6946	0.3941	0.9697
5.9304	30000	0.676	0.3940	0.9700

Framework Versions

• Python: 3.11.13
• Sentence Transformers: 5.1.2
• Transformers: 4.53.3
• PyTorch: 2.6.0+cu124
• Accelerate: 1.9.0
• Datasets: 4.4.1
• Tokenizers: 0.21.2

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}